Fall detection via human posture representation and support vector machine
Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision–based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717707418 |
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| _version_ | 1832555320497405952 |
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| author | Kaibo Fan Ping Wang Yan Hu Bingjie Dou |
| author_facet | Kaibo Fan Ping Wang Yan Hu Bingjie Dou |
| author_sort | Kaibo Fan |
| collection | DOAJ |
| description | Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision–based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multi-directional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset. |
| format | Article |
| id | doaj-art-c6c2ae9e30ea4fecb326bc43e0417b98 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c6c2ae9e30ea4fecb326bc43e0417b982025-02-03T05:48:32ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-05-011310.1177/1550147717707418Fall detection via human posture representation and support vector machineKaibo FanPing WangYan HuBingjie DouAccidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision–based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multi-directional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.https://doi.org/10.1177/1550147717707418 |
| spellingShingle | Kaibo Fan Ping Wang Yan Hu Bingjie Dou Fall detection via human posture representation and support vector machine International Journal of Distributed Sensor Networks |
| title | Fall detection via human posture representation and support vector machine |
| title_full | Fall detection via human posture representation and support vector machine |
| title_fullStr | Fall detection via human posture representation and support vector machine |
| title_full_unstemmed | Fall detection via human posture representation and support vector machine |
| title_short | Fall detection via human posture representation and support vector machine |
| title_sort | fall detection via human posture representation and support vector machine |
| url | https://doi.org/10.1177/1550147717707418 |
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